Artificial cognition for applications in smart agriculture: A comprehensive review

Prediction of energy consumption and price is crucial in formatting policies related to the global energy market, demand, and supply. Data-driven analysis methods are giving rise to innovations in the world energy sector, including energy finance and economics. This paper has critically evaluated expand writings committed to Energy finance and economics applications of data-driven analysis. This paper comes up with an extensive view of state of the art in the area, which is already discussed with a different procedure. This review recognizes the applications of data-driven analysis methods in various areas such as forecasting domestic, nationwide, and transport energy consumption and price forecasting of multiple commodities, including crude oil, natural gas, and electricity. We have investigated certain research papers and given conclusion based on their researched and proposed model's prediction results and accuracies in respective areas. Our study suggests that Artificial Neural Network (ANN), Support Vector Machine (SVM), and other proposed neural network models are the most effective methods among other statistical and machine learning methods.

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“…There can be a more hidden layer. The number of hidden layers depends on the problem's complexity [8][9][10]34].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

Patel

Shah

2021

SN COMPUT. SCI.

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“…Machine learning (ML) is an application that provides a system with the ability to learn and improve automatically from past experiences without being explicitly programmed [6][7][8]. After viewing the data, an exact pattern or information cannot always be determined [9][10][11]. In such cases, ML is applied to interpret the exact pattern and information [12,13].…”

Section: Introductionmentioning

confidence: 99%

Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention

Shah¹,

Shah

2021

Vis. Comput. Ind. Biomed. Art

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A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient. Thus, if we can come up with ways to predict crime, in detail, before it occurs, or come up with a “machine” that can assist police officers, it would lift the burden of police and help in preventing crimes. To achieve this, we suggest including machine learning (ML) and computer vision algorithms and techniques. In this paper, we describe the results of certain cases where such approaches were used, and which motivated us to pursue further research in this field. The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques. The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect, prevent, and solve crimes at a much more accurate and faster rate. In summary, ML and computer vision techniques can bring about an evolution in law agencies.

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“…In recent years, machine learning has been widely applied to several agricultural studies, and some results have been achieved [3]. Researchers have applied computer vision techniques in apple quality inspection [4][5][6], apple pesticide residues [7], apple size assessment [8,9], and apple detection [10].…”

Section: Introductionmentioning

confidence: 99%

Using Generative Module and Pruning Inference for the Fast and Accurate Detection of Apple Flower in Natural Environments

Zhang

et al. 2021

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Apple flower detection is an important project in the apple planting stage. This paper proposes an optimized detection network model based on a generative module and pruning inference. Due to the problems of instability, non-convergence, and overfitting of convolutional neural networks in the case of insufficient samples, this paper uses a generative module and various image pre-processing methods including Cutout, CutMix, Mixup, SnapMix, and Mosaic algorithms for data augmentation. In order to solve the problem of slowing down the training and inference due to the increasing complexity of detection networks, the pruning inference proposed in this paper can automatically deactivate part of the network structure according to the different conditions, reduce the network parameters and operations, and significantly improve the network speed. The proposed model can achieve 90.01%, 98.79%, and 97.43% in precision, recall, and mAP, respectively, in detecting the apple flowers, and the inference speed can reach 29 FPS. On the YOLO-v5 model with slightly lower performance, the inference speed can reach 71 FPS by the pruning inference. These experimental results demonstrate that the model proposed in this paper can meet the needs of agricultural production.

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Artificial cognition for applications in smart agriculture: A comprehensive review

Cited by 110 publications

References 98 publications

Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention

Using Generative Module and Pruning Inference for the Fast and Accurate Detection of Apple Flower in Natural Environments

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